Factors affecting the duration of native and second-language sentences produced in unspeeded and speeded conditions
Bibliographic record
Abstract
The aim of this study was to provide insight into why non-natives generally produce longer second-language (L2) sentences than native speakers do. Four groups of 16 Italian–English bilinguals were recruited in Ottawa based on orthogonal differences in age of arrival (AOA) to Canada from Italy (early versus late) and self-reported percentage Italian use (low-L1 use versus high-L1 use). The bilinguals repeated duration-matched English and Italian sentences presented via a loudspeaker in an ‘‘unspeeded’’ condition, then as rapidly as possible (the ‘‘speeded’’ condition). The same effect of AOA was obtained in both conditions despite a 20% reduction in sentence duration in the speeded condition. That is, the early bilinguals produced significantly shorter English than Italian sentences, whereas the late bilinguals produced significantly longer English than Italian sentences. Different L1 use effects were obtained in the two conditions, however. In the unspeeded condition, the low-L1-use bilinguals produced shorter English than Italian sentences, with a nonsignificant effect of language for high-L1-use bilinguals. In the speeded condition, high-L1-use bilinguals produced shorter Italian than English sentences, with no language effect for the low-L1-use bilinguals. The underlying bases of AOA and L1 use effects on L2 sentence production will be discussed. [Work supported by NIH.]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".